Overview

Dataset statistics

Number of variables20
Number of observations450000
Missing cells1816686
Missing cells (%)20.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.7 MiB
Average record size in memory160.0 B

Variable types

Categorical11
Numeric9

Alerts

reassigned_order has constant value "1.0" Constant
order_time has a high cardinality: 252868 distinct values High cardinality
allot_time has a high cardinality: 246871 distinct values High cardinality
accept_time has a high cardinality: 254201 distinct values High cardinality
pickup_time has a high cardinality: 257117 distinct values High cardinality
delivered_time has a high cardinality: 257067 distinct values High cardinality
cancelled_time has a high cardinality: 5176 distinct values High cardinality
alloted_orders is highly correlated with delivered_orders and 1 other fieldsHigh correlation
delivered_orders is highly correlated with alloted_orders and 1 other fieldsHigh correlation
lifetime_order_count is highly correlated with alloted_orders and 1 other fieldsHigh correlation
alloted_orders is highly correlated with delivered_ordersHigh correlation
delivered_orders is highly correlated with alloted_ordersHigh correlation
alloted_orders is highly correlated with delivered_ordersHigh correlation
delivered_orders is highly correlated with alloted_ordersHigh correlation
cancelled is highly correlated with reassigned_orderHigh correlation
reassignment_reason is highly correlated with reassigned_orderHigh correlation
order_date is highly correlated with reassigned_orderHigh correlation
reassigned_order is highly correlated with cancelled and 3 other fieldsHigh correlation
reassignment_method is highly correlated with reassigned_orderHigh correlation
order_id is highly correlated with order_dateHigh correlation
order_date is highly correlated with order_idHigh correlation
alloted_orders is highly correlated with delivered_orders and 1 other fieldsHigh correlation
delivered_orders is highly correlated with alloted_orders and 1 other fieldsHigh correlation
undelivered_orders is highly correlated with alloted_orders and 1 other fieldsHigh correlation
delivered_time has 5218 (1.2%) missing values Missing
alloted_orders has 16948 (3.8%) missing values Missing
delivered_orders has 17341 (3.9%) missing values Missing
undelivered_orders has 17341 (3.9%) missing values Missing
reassignment_method has 436256 (96.9%) missing values Missing
reassignment_reason has 436247 (96.9%) missing values Missing
reassigned_order has 436247 (96.9%) missing values Missing
cancelled_time has 444782 (98.8%) missing values Missing
order_time is uniformly distributed Uniform
allot_time is uniformly distributed Uniform
accept_time is uniformly distributed Uniform
pickup_time is uniformly distributed Uniform
delivered_time is uniformly distributed Uniform
cancelled_time is uniformly distributed Uniform
undelivered_orders has 232686 (51.7%) zeros Zeros

Reproduction

Analysis started2022-02-13 05:37:34.729919
Analysis finished2022-02-13 05:39:21.262105
Duration1 minute and 46.53 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

order_time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct252868
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2021-01-30 14:38:36
 
11
2021-01-30 13:30:43
 
11
2021-01-26 13:57:44
 
10
2021-01-30 13:33:54
 
10
2021-02-03 14:11:49
 
10
Other values (252863)
449948 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133840 ?
Unique (%)29.7%

Sample

1st row2021-01-26 02:21:35
2nd row2021-01-26 02:33:16
3rd row2021-01-26 02:39:49
4th row2021-01-26 02:47:53
5th row2021-01-26 03:06:30

Common Values

ValueCountFrequency (%)
2021-01-30 14:38:3611
 
< 0.1%
2021-01-30 13:30:4311
 
< 0.1%
2021-01-26 13:57:4410
 
< 0.1%
2021-01-30 13:33:5410
 
< 0.1%
2021-02-03 14:11:4910
 
< 0.1%
2021-02-03 13:42:5510
 
< 0.1%
2021-01-30 13:33:3710
 
< 0.1%
2021-02-02 13:35:1410
 
< 0.1%
2021-01-28 15:43:0510
 
< 0.1%
2021-01-29 14:30:3210
 
< 0.1%
Other values (252858)449898
> 99.9%

Length

2022-02-13T11:09:21.388765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-02-0542341
 
4.7%
2021-02-0341447
 
4.6%
2021-01-2941207
 
4.6%
2021-02-0240875
 
4.5%
2021-01-2840783
 
4.5%
2021-02-0439685
 
4.4%
2021-01-3139254
 
4.4%
2021-01-2739015
 
4.3%
2021-01-2638090
 
4.2%
2021-02-0137833
 
4.2%
Other values (43385)499470
55.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct449999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369143.0808
Minimum118350
Maximum594842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:21.638890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum118350
5-th percentile167343.95
Q1257342.75
median369842.5
Q3482342.25
95-th percentile572342.05
Maximum594842
Range476492
Interquartile range (IQR)224999.5

Descriptive statistics

Standard deviation131146.9064
Coefficient of variation (CV)0.355273912
Kurtosis-1.149146297
Mean369143.0808
Median Absolute Deviation (MAD)112500
Skewness-0.03426737717
Sum1.661143863 × 1011
Variance1.719951106 × 1010
MonotonicityNot monotonic
2022-02-13T11:09:21.949198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1814022
 
< 0.1%
5567531
 
< 0.1%
3064221
 
< 0.1%
3064331
 
< 0.1%
3064301
 
< 0.1%
3064291
 
< 0.1%
3064281
 
< 0.1%
3064311
 
< 0.1%
3064321
 
< 0.1%
3064271
 
< 0.1%
Other values (449989)449989
> 99.9%
ValueCountFrequency (%)
1183501
< 0.1%
1183511
< 0.1%
1183521
< 0.1%
1183531
< 0.1%
1183541
< 0.1%
1183551
< 0.1%
1183561
< 0.1%
1183571
< 0.1%
1183581
< 0.1%
1183591
< 0.1%
ValueCountFrequency (%)
5948421
< 0.1%
5948411
< 0.1%
5948401
< 0.1%
5948391
< 0.1%
5948381
< 0.1%
5948371
< 0.1%
5948361
< 0.1%
5948351
< 0.1%
5948341
< 0.1%
5948331
< 0.1%

order_date
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2021-02-05 00:00:00
42341 
2021-02-03 00:00:00
41447 
2021-01-29 00:00:00
41207 
2021-02-02 00:00:00
40875 
2021-01-28 00:00:00
40783 
Other values (7)
243347 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-26 00:00:00
2nd row2021-01-26 00:00:00
3rd row2021-01-26 00:00:00
4th row2021-01-26 00:00:00
5th row2021-01-26 00:00:00

Common Values

ValueCountFrequency (%)
2021-02-05 00:00:0042341
9.4%
2021-02-03 00:00:0041447
9.2%
2021-01-29 00:00:0041207
9.2%
2021-02-02 00:00:0040875
9.1%
2021-01-28 00:00:0040783
9.1%
2021-02-04 00:00:0039685
8.8%
2021-01-31 00:00:0039254
8.7%
2021-01-27 00:00:0039015
8.7%
2021-01-26 00:00:0038090
8.5%
2021-02-01 00:00:0037833
8.4%
Other values (2)49470
11.0%

Length

2022-02-13T11:09:22.198814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00450000
50.0%
2021-02-0542341
 
4.7%
2021-02-0341447
 
4.6%
2021-01-2941207
 
4.6%
2021-02-0240875
 
4.5%
2021-01-2840783
 
4.5%
2021-02-0439685
 
4.4%
2021-01-3139254
 
4.4%
2021-01-2739015
 
4.3%
2021-01-2638090
 
4.2%
Other values (3)87303
 
9.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

allot_time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct246871
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
2021-01-29 14:18:11
 
13
2021-02-03 14:56:54
 
13
2021-01-29 17:12:47
 
12
2021-01-29 16:19:15
 
12
2021-01-30 13:33:29
 
12
Other values (246866)
449938 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129910 ?
Unique (%)28.9%

Sample

1st row2021-01-26 02:21:59
2nd row2021-01-26 02:33:57
3rd row2021-01-26 02:39:57
4th row2021-01-26 02:48:25
5th row2021-01-26 03:07:21

Common Values

ValueCountFrequency (%)
2021-01-29 14:18:1113
 
< 0.1%
2021-02-03 14:56:5413
 
< 0.1%
2021-01-29 17:12:4712
 
< 0.1%
2021-01-29 16:19:1512
 
< 0.1%
2021-01-30 13:33:2912
 
< 0.1%
2021-02-01 15:30:4612
 
< 0.1%
2021-02-05 16:01:4511
 
< 0.1%
2021-02-04 17:16:5511
 
< 0.1%
2021-01-29 13:43:1211
 
< 0.1%
2021-01-30 14:07:2310
 
< 0.1%
Other values (246861)449883
> 99.9%

Length

2022-02-13T11:09:22.578885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-02-0542341
 
4.7%
2021-02-0341447
 
4.6%
2021-01-2941207
 
4.6%
2021-02-0240875
 
4.5%
2021-01-2840783
 
4.5%
2021-02-0439685
 
4.4%
2021-01-3139254
 
4.4%
2021-01-2739015
 
4.3%
2021-01-2638090
 
4.2%
2021-02-0137833
 
4.2%
Other values (43527)499470
55.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

accept_time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct254201
Distinct (%)56.5%
Missing157
Missing (%)< 0.1%
Memory size3.4 MiB
2021-01-27 16:03:12
 
15
2021-02-01 13:32:55
 
13
2021-01-31 13:31:30
 
12
2021-01-29 13:35:13
 
11
2021-01-28 13:34:46
 
11
Other values (254196)
449781 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135348 ?
Unique (%)30.1%

Sample

1st row2021-01-26 02:22:08
2nd row2021-01-26 02:34:45
3rd row2021-01-26 02:40:13
4th row2021-01-26 02:49:06
5th row2021-01-26 03:07:57

Common Values

ValueCountFrequency (%)
2021-01-27 16:03:1215
 
< 0.1%
2021-02-01 13:32:5513
 
< 0.1%
2021-01-31 13:31:3012
 
< 0.1%
2021-01-29 13:35:1311
 
< 0.1%
2021-01-28 13:34:4611
 
< 0.1%
2021-02-05 16:31:4710
 
< 0.1%
2021-01-31 17:03:3610
 
< 0.1%
2021-02-01 15:33:3410
 
< 0.1%
2021-02-05 14:47:1210
 
< 0.1%
2021-01-28 14:24:3310
 
< 0.1%
Other values (254191)449731
99.9%
(Missing)157
 
< 0.1%

Length

2022-02-13T11:09:22.798742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-02-0542325
 
4.7%
2021-02-0341436
 
4.6%
2021-01-2941192
 
4.6%
2021-02-0240864
 
4.5%
2021-01-2840775
 
4.5%
2021-02-0439673
 
4.4%
2021-01-3139236
 
4.4%
2021-01-2739006
 
4.3%
2021-01-2638076
 
4.2%
2021-02-0137815
 
4.2%
Other values (43911)499288
55.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

pickup_time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct257117
Distinct (%)57.4%
Missing2421
Missing (%)0.5%
Memory size3.4 MiB
2021-01-28 15:29:58
 
11
2021-01-30 14:12:40
 
10
2021-01-28 15:52:01
 
10
2021-01-29 14:50:58
 
10
2021-02-05 15:06:01
 
10
Other values (257112)
447528 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139375 ?
Unique (%)31.1%

Sample

1st row2021-01-26 02:32:51
2nd row2021-01-26 02:50:25
3rd row2021-01-26 02:56:00
4th row2021-01-26 03:21:51
5th row2021-01-26 03:31:38

Common Values

ValueCountFrequency (%)
2021-01-28 15:29:5811
 
< 0.1%
2021-01-30 14:12:4010
 
< 0.1%
2021-01-28 15:52:0110
 
< 0.1%
2021-01-29 14:50:5810
 
< 0.1%
2021-02-05 15:06:0110
 
< 0.1%
2021-01-30 15:22:4010
 
< 0.1%
2021-02-02 16:14:3710
 
< 0.1%
2021-01-30 15:22:1110
 
< 0.1%
2021-02-02 14:19:5010
 
< 0.1%
2021-02-05 14:51:149
 
< 0.1%
Other values (257107)447479
99.4%
(Missing)2421
 
0.5%

Length

2022-02-13T11:09:23.012623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-02-0542117
 
4.7%
2021-02-0341247
 
4.6%
2021-01-2941010
 
4.6%
2021-02-0240674
 
4.5%
2021-01-2840585
 
4.5%
2021-02-0439511
 
4.4%
2021-01-3139065
 
4.4%
2021-01-2738824
 
4.3%
2021-01-2637883
 
4.2%
2021-02-0137667
 
4.2%
Other values (44674)496575
55.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

delivered_time
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct257067
Distinct (%)57.8%
Missing5218
Missing (%)1.2%
Memory size3.4 MiB
2021-01-30 14:38:14
 
12
2021-02-01 16:27:22
 
9
2021-01-31 15:45:09
 
9
2021-02-01 15:54:50
 
9
2021-01-29 14:26:56
 
9
Other values (257062)
444734 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique140784 ?
Unique (%)31.7%

Sample

1st row2021-01-26 02:49:47
2nd row2021-01-26 03:11:15
3rd row2021-01-26 03:12:46
4th row2021-01-26 03:41:05
5th row2021-01-26 04:00:15

Common Values

ValueCountFrequency (%)
2021-01-30 14:38:1412
 
< 0.1%
2021-02-01 16:27:229
 
< 0.1%
2021-01-31 15:45:099
 
< 0.1%
2021-02-01 15:54:509
 
< 0.1%
2021-01-29 14:26:569
 
< 0.1%
2021-01-30 14:38:189
 
< 0.1%
2021-02-05 15:53:029
 
< 0.1%
2021-01-27 14:25:539
 
< 0.1%
2021-01-28 16:38:249
 
< 0.1%
2021-02-03 15:41:309
 
< 0.1%
Other values (257057)444689
98.8%
(Missing)5218
 
1.2%

Length

2022-02-13T11:09:23.223817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-02-0541842
 
4.7%
2021-02-0340985
 
4.6%
2021-01-2940754
 
4.6%
2021-02-0240461
 
4.5%
2021-01-2840292
 
4.5%
2021-02-0439262
 
4.4%
2021-01-3138899
 
4.4%
2021-01-2738609
 
4.3%
2021-01-2637628
 
4.2%
2021-02-0137450
 
4.2%
Other values (45275)493382
55.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rider_id
Real number (ℝ≥0)

Distinct19537
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7763.244016
Minimum0
Maximum21566
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:23.463199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519
Q12805
median6754
Q311965
95-th percentile18050
Maximum21566
Range21566
Interquartile range (IQR)9160

Descriptive statistics

Standard deviation5592.880135
Coefficient of variation (CV)0.7204308049
Kurtosis-0.8427443266
Mean7763.244016
Median Absolute Deviation (MAD)4436
Skewness0.4790871039
Sum3493459807
Variance31280308.2
MonotonicityNot monotonic
2022-02-13T11:09:23.748785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237228
 
0.1%
190213
 
< 0.1%
11998209
 
< 0.1%
170203
 
< 0.1%
853200
 
< 0.1%
747199
 
< 0.1%
2280195
 
< 0.1%
4921194
 
< 0.1%
12709191
 
< 0.1%
453190
 
< 0.1%
Other values (19527)447978
99.6%
ValueCountFrequency (%)
07
 
< 0.1%
116
 
< 0.1%
228
 
< 0.1%
43
 
< 0.1%
519
 
< 0.1%
615
 
< 0.1%
929
 
< 0.1%
1097
< 0.1%
1125
 
< 0.1%
1224
 
< 0.1%
ValueCountFrequency (%)
215661
< 0.1%
215651
< 0.1%
215641
< 0.1%
215631
< 0.1%
215621
< 0.1%
215611
< 0.1%
215602
< 0.1%
215592
< 0.1%
215582
< 0.1%
215571
< 0.1%

first_mile_distance
Real number (ℝ≥0)

Distinct93743
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.229888977
Minimum0.0001342587859
Maximum42.0381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:24.045558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0001342587859
5-th percentile0.05458151785
Q10.539575
median1.1387
Q31.853
95-th percentile2.7079
Maximum42.0381
Range42.03796574
Interquartile range (IQR)1.313425

Descriptive statistics

Standard deviation0.8461826096
Coefficient of variation (CV)0.688015443
Kurtosis12.36678626
Mean1.229888977
Median Absolute Deviation (MAD)0.6482
Skewness0.7588916893
Sum553450.0398
Variance0.7160250088
MonotonicityNot monotonic
2022-02-13T11:09:24.328708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.715339
 
< 0.1%
0.740439
 
< 0.1%
0.696536
 
< 0.1%
0.695936
 
< 0.1%
0.724134
 
< 0.1%
0.657834
 
< 0.1%
0.688334
 
< 0.1%
1.000934
 
< 0.1%
1.535634
 
< 0.1%
0.617434
 
< 0.1%
Other values (93733)449646
99.9%
ValueCountFrequency (%)
0.00013425878592
 
< 0.1%
0.00021228177971
 
< 0.1%
0.00028480589391
 
< 0.1%
0.0003288655191
 
< 0.1%
0.00037974119192
 
< 0.1%
0.00040277635785
< 0.1%
0.000413813371
 
< 0.1%
0.00044528601772
 
< 0.1%
0.00045529369473
< 0.1%
0.00046508607722
 
< 0.1%
ValueCountFrequency (%)
42.03811
< 0.1%
17.34421
< 0.1%
11.67411
< 0.1%
11.36491
< 0.1%
10.75251
< 0.1%
10.69941
< 0.1%
10.58391
< 0.1%
10.05461
< 0.1%
9.66951
< 0.1%
9.60791
< 0.1%

last_mile_distance
Real number (ℝ≥0)

Distinct1331
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.968872711
Minimum0
Maximum22.41
Zeros94
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:24.629258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11.47
median2.67
Q34.22
95-th percentile6.22
Maximum22.41
Range22.41
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.884123753
Coefficient of variation (CV)0.6346259797
Kurtosis0.8795462088
Mean2.968872711
Median Absolute Deviation (MAD)1.33
Skewness0.8267706027
Sum1335992.72
Variance3.549922316
MonotonicityNot monotonic
2022-02-13T11:09:24.899164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.581143
 
0.3%
1.51121
 
0.2%
1.971119
 
0.2%
1.451111
 
0.2%
1.631110
 
0.2%
1.461109
 
0.2%
1.541107
 
0.2%
1.321105
 
0.2%
1.761104
 
0.2%
1.671104
 
0.2%
Other values (1321)438867
97.5%
ValueCountFrequency (%)
094
 
< 0.1%
0.01170
 
< 0.1%
0.02210
< 0.1%
0.03193
< 0.1%
0.04237
0.1%
0.05253
0.1%
0.06291
0.1%
0.07353
0.1%
0.08415
0.1%
0.09482
0.1%
ValueCountFrequency (%)
22.411
< 0.1%
22.261
< 0.1%
21.271
< 0.1%
21.211
< 0.1%
21.21
< 0.1%
21.121
< 0.1%
19.811
< 0.1%
18.131
< 0.1%
17.271
< 0.1%
16.851
< 0.1%

alloted_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct510
Distinct (%)0.1%
Missing16948
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean104.6209093
Minimum1
Maximum567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:25.179602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q136
median81
Q3147
95-th percentile289
Maximum567
Range566
Interquartile range (IQR)111

Descriptive statistics

Standard deviation90.13549192
Coefficient of variation (CV)0.8615437636
Kurtosis2.098562998
Mean104.6209093
Median Absolute Deviation (MAD)52
Skewness1.384162248
Sum45306294
Variance8124.406904
MonotonicityNot monotonic
2022-02-13T11:09:25.448988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63764
 
0.8%
53717
 
0.8%
43648
 
0.8%
23589
 
0.8%
73586
 
0.8%
33513
 
0.8%
103424
 
0.8%
83309
 
0.7%
93168
 
0.7%
123132
 
0.7%
Other values (500)398202
88.5%
(Missing)16948
 
3.8%
ValueCountFrequency (%)
12991
0.7%
23589
0.8%
33513
0.8%
43648
0.8%
53717
0.8%
63764
0.8%
73586
0.8%
83309
0.7%
93168
0.7%
103424
0.8%
ValueCountFrequency (%)
56712
< 0.1%
56518
< 0.1%
56317
< 0.1%
56118
< 0.1%
5589
 
< 0.1%
55525
< 0.1%
55321
< 0.1%
54528
< 0.1%
54021
< 0.1%
53620
< 0.1%

delivered_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct504
Distinct (%)0.1%
Missing17341
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean103.9504483
Minimum1
Maximum562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:25.745168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q136
median81
Q3146
95-th percentile288
Maximum562
Range561
Interquartile range (IQR)110

Descriptive statistics

Standard deviation89.63964602
Coefficient of variation (CV)0.8623305383
Kurtosis2.117421984
Mean103.9504483
Median Absolute Deviation (MAD)52
Skewness1.389351151
Sum44975097
Variance8035.266138
MonotonicityNot monotonic
2022-02-13T11:09:26.018927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63798
 
0.8%
43698
 
0.8%
53697
 
0.8%
73645
 
0.8%
33528
 
0.8%
23525
 
0.8%
103403
 
0.8%
83322
 
0.7%
133228
 
0.7%
203207
 
0.7%
Other values (494)397608
88.4%
(Missing)17341
 
3.9%
ValueCountFrequency (%)
12949
0.7%
23525
0.8%
33528
0.8%
43698
0.8%
53697
0.8%
63798
0.8%
73645
0.8%
83322
0.7%
93180
0.7%
103403
0.8%
ValueCountFrequency (%)
56212
< 0.1%
56018
< 0.1%
55917
< 0.1%
55718
< 0.1%
5539
 
< 0.1%
55025
< 0.1%
54821
< 0.1%
54028
< 0.1%
53521
< 0.1%
53120
< 0.1%

cancelled
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0
444782 
1
 
5218

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0444782
98.8%
15218
 
1.2%

Length

2022-02-13T11:09:26.310278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-13T11:09:26.445589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0444782
98.8%
15218
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

undelivered_orders
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing17341
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean0.7641653126
Minimum0
Maximum9
Zeros232686
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:26.569063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.066473491
Coefficient of variation (CV)1.395605732
Kurtosis4.67331547
Mean0.7641653126
Median Absolute Deviation (MAD)0
Skewness1.848054475
Sum330623
Variance1.137365706
MonotonicityNot monotonic
2022-02-13T11:09:26.912432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0232686
51.7%
1118103
26.2%
250582
 
11.2%
320011
 
4.4%
47733
 
1.7%
51851
 
0.4%
6932
 
0.2%
7600
 
0.1%
8105
 
< 0.1%
956
 
< 0.1%
(Missing)17341
 
3.9%
ValueCountFrequency (%)
0232686
51.7%
1118103
26.2%
250582
 
11.2%
320011
 
4.4%
47733
 
1.7%
51851
 
0.4%
6932
 
0.2%
7600
 
0.1%
8105
 
< 0.1%
956
 
< 0.1%
ValueCountFrequency (%)
956
 
< 0.1%
8105
 
< 0.1%
7600
 
0.1%
6932
 
0.2%
51851
 
0.4%
47733
 
1.7%
320011
 
4.4%
250582
 
11.2%
1118103
26.2%
0232686
51.7%

lifetime_order_count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2887
Distinct (%)0.6%
Missing53
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean853.6406643
Minimum0
Maximum30469
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:27.148874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1165
median396
Q3948
95-th percentile3013
Maximum30469
Range30469
Interquartile range (IQR)783

Descriptive statistics

Standard deviation1502.976162
Coefficient of variation (CV)1.76066608
Kurtosis77.96603641
Mean853.6406643
Median Absolute Deviation (MAD)287
Skewness6.756972165
Sum384093056
Variance2258937.343
MonotonicityNot monotonic
2022-02-13T11:09:27.419880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
501609
 
0.4%
511230
 
0.3%
2901201
 
0.3%
1301170
 
0.3%
1661162
 
0.3%
2281161
 
0.3%
991104
 
0.2%
841091
 
0.2%
571086
 
0.2%
1061084
 
0.2%
Other values (2877)438049
97.3%
ValueCountFrequency (%)
05
 
< 0.1%
1172
< 0.1%
2232
0.1%
3293
0.1%
4280
0.1%
5381
0.1%
6344
0.1%
7428
0.1%
8369
0.1%
9375
0.1%
ValueCountFrequency (%)
3046950
< 0.1%
2797255
< 0.1%
2681026
 
< 0.1%
249334
 
< 0.1%
2402280
< 0.1%
2362633
< 0.1%
2258134
< 0.1%
2203327
 
< 0.1%
2199510
 
< 0.1%
219387
 
< 0.1%

reassignment_method
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing436256
Missing (%)96.9%
Memory size3.4 MiB
auto
13383 
manual
 
361

Length

Max length6
Median length4
Mean length4.052532014
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowauto
2nd rowauto
3rd rowauto
4th rowauto
5th rowauto

Common Values

ValueCountFrequency (%)
auto13383
 
3.0%
manual361
 
0.1%
(Missing)436256
96.9%

Length

2022-02-13T11:09:27.688848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-13T11:09:27.854955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
auto13383
97.4%
manual361
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reassignment_reason
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing436247
Missing (%)96.9%
Memory size3.4 MiB
Auto Reassignment basis Inaction. coreengine.tasks.repush_order_to_aa_bucket
7212 
Reassignment Request from SE portal.
5300 
Reassign
1241 

Length

Max length76
Median length76
Mean length54.44921108
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReassignment Request from SE portal.
2nd rowReassignment Request from SE portal.
3rd rowAuto Reassignment basis Inaction. coreengine.tasks.repush_order_to_aa_bucket
4th rowAuto Reassignment basis Inaction. coreengine.tasks.repush_order_to_aa_bucket
5th rowReassignment Request from SE portal.

Common Values

ValueCountFrequency (%)
Auto Reassignment basis Inaction. coreengine.tasks.repush_order_to_aa_bucket7212
 
1.6%
Reassignment Request from SE portal.5300
 
1.2%
Reassign1241
 
0.3%
(Missing)436247
96.9%

Length

2022-02-13T11:09:28.010601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-13T11:09:28.173871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
reassignment12512
19.6%
auto7212
11.3%
basis7212
11.3%
inaction7212
11.3%
coreengine.tasks.repush_order_to_aa_bucket7212
11.3%
request5300
8.3%
from5300
8.3%
se5300
8.3%
portal5300
8.3%
reassign1241
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reassigned_order
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing436247
Missing (%)96.9%
Memory size3.4 MiB
1.0
13753 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.013753
 
3.1%
(Missing)436247
96.9%

Length

2022-02-13T11:09:28.361640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-13T11:09:28.500815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.013753
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

session_time
Real number (ℝ≥0)

Distinct65872
Distinct (%)14.8%
Missing3675
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean220.4747785
Minimum0
Maximum1298.966667
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-13T11:09:28.668860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.8
Q184.1
median175.55
Q3316.7666667
95-th percentile581.2833333
Maximum1298.966667
Range1298.966667
Interquartile range (IQR)232.6666667

Descriptive statistics

Standard deviation176.713853
Coefficient of variation (CV)0.8015150494
Kurtosis0.3002308299
Mean220.4747785
Median Absolute Deviation (MAD)104.2
Skewness0.986198014
Sum98403405.53
Variance31227.78585
MonotonicityNot monotonic
2022-02-13T11:09:28.969778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06666666667155
 
< 0.1%
0.03333333333144
 
< 0.1%
0.1166666667136
 
< 0.1%
0.15132
 
< 0.1%
0.05129
 
< 0.1%
0.08333333333124
 
< 0.1%
240120
 
< 0.1%
0.1118
 
< 0.1%
0.1333333333117
 
< 0.1%
0.1666666667117
 
< 0.1%
Other values (65862)445033
98.9%
(Missing)3675
 
0.8%
ValueCountFrequency (%)
016
 
< 0.1%
0.01666666667115
< 0.1%
0.03333333333144
< 0.1%
0.05129
< 0.1%
0.06666666667155
< 0.1%
0.08333333333124
< 0.1%
0.1118
< 0.1%
0.1166666667136
< 0.1%
0.1333333333117
< 0.1%
0.15132
< 0.1%
ValueCountFrequency (%)
1298.9666671
< 0.1%
1294.4333331
< 0.1%
1250.3333331
< 0.1%
1183.5666671
< 0.1%
1141.5333331
< 0.1%
1096.8333331
< 0.1%
1076.851
< 0.1%
1057.4833331
< 0.1%
1043.8666671
< 0.1%
1038.2166671
< 0.1%

cancelled_time
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct5176
Distinct (%)99.2%
Missing444782
Missing (%)98.8%
Memory size3.4 MiB
2021-01-29 17:02:50
 
2
2021-01-30 16:10:49
 
2
2021-01-30 16:51:46
 
2
2021-02-01 14:44:04
 
2
2021-02-03 15:16:23
 
2
Other values (5171)
5208 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5134 ?
Unique (%)98.4%

Sample

1st row2021-01-26 04:51:46
2nd row2021-01-26 04:08:50
3rd row2021-01-26 05:45:51
4th row2021-01-26 05:10:15
5th row2021-01-26 04:40:46

Common Values

ValueCountFrequency (%)
2021-01-29 17:02:502
 
< 0.1%
2021-01-30 16:10:492
 
< 0.1%
2021-01-30 16:51:462
 
< 0.1%
2021-02-01 14:44:042
 
< 0.1%
2021-02-03 15:16:232
 
< 0.1%
2021-01-28 17:27:572
 
< 0.1%
2021-01-30 15:06:392
 
< 0.1%
2021-01-27 16:42:162
 
< 0.1%
2021-01-30 15:47:212
 
< 0.1%
2021-01-30 15:45:222
 
< 0.1%
Other values (5166)5198
 
1.2%
(Missing)444782
98.8%

Length

2022-02-13T11:09:29.239090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-30723
 
6.9%
2021-02-05496
 
4.8%
2021-01-28483
 
4.6%
2021-02-03464
 
4.4%
2021-01-29448
 
4.3%
2021-02-04433
 
4.1%
2021-02-02428
 
4.1%
2021-01-27427
 
4.1%
2021-01-26421
 
4.0%
2021-01-31394
 
3.8%
Other values (4820)5719
54.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-02-13T11:09:07.162030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:43.238618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:45.099903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:46.998981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:48.948910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:51.230529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:53.318809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:56.996650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:01.938937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:07.770108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:43.462793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:45.319246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:47.209164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:49.159085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:51.452430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:53.550161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:57.548958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:02.539732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:08.363021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:43.664098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:45.529185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:47.399833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:49.358819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:51.669868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:53.762137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:58.049604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:03.103960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:08.904079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:43.865418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:45.743865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:47.609055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:49.578927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:51.894983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:54.130756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:58.577477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:03.694606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:09.471457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:44.062610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:45.950184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:47.819176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:49.922429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:52.208672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:54.419098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:59.113232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:04.247016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:10.048641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:44.262801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:46.161683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:48.110065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:50.173227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:52.430632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:54.698583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:59.613400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:04.769462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:10.765631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:44.469041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:46.369198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:48.322882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:50.450642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:52.661446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:55.288602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:00.169577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:05.278937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:11.396110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:44.669251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:46.588340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:48.526567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:50.706302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:52.881742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:55.878917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:00.702559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:05.829473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:12.139141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:44.879276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:46.798768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:48.729033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:50.974991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:53.099029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:08:56.450482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:01.295659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T11:09:06.392600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-13T11:09:29.438654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-13T11:09:29.862654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-13T11:09:30.288458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-13T11:09:30.678713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-13T11:09:30.984045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-13T11:09:12.905809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-13T11:09:14.880630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-13T11:09:18.381594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-13T11:09:19.395347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

order_timeorder_idorder_dateallot_timeaccept_timepickup_timedelivered_timerider_idfirst_mile_distancelast_mile_distancealloted_ordersdelivered_orderscancelledundelivered_orderslifetime_order_countreassignment_methodreassignment_reasonreassigned_ordersession_timecancelled_time
02021-01-26 02:21:355567532021-01-26 00:00:002021-01-26 02:21:592021-01-26 02:22:082021-01-26 02:32:512021-01-26 02:49:47116961.5666002.6546.046.000.0621.0NaNNaNNaNNaNNaN
12021-01-26 02:33:165567542021-01-26 00:00:002021-01-26 02:33:572021-01-26 02:34:452021-01-26 02:50:252021-01-26 03:11:15181172.5207002.768.08.000.0105.0NaNNaNNaN3.266667NaN
22021-01-26 02:39:495567552021-01-26 00:00:002021-01-26 02:39:572021-01-26 02:40:132021-01-26 02:56:002021-01-26 03:12:46186232.2074004.801.01.000.066.0NaNNaNNaN9.816667NaN
32021-01-26 02:47:535567562021-01-26 00:00:002021-01-26 02:48:252021-01-26 02:49:062021-01-26 03:21:512021-01-26 03:41:05159452.1894006.381.01.000.0127.0NaNNaNNaN17.533333NaN
42021-01-26 03:06:305567572021-01-26 00:00:002021-01-26 03:07:212021-01-26 03:07:572021-01-26 03:31:382021-01-26 04:00:15175892.7870004.0134.034.000.084.0NaNNaNNaN1.350000NaN
52021-01-26 03:07:165567582021-01-26 00:00:002021-01-26 03:12:142021-01-26 03:12:272021-01-26 03:25:362021-01-26 03:45:5114692.4818005.18296.0294.002.01506.0NaNNaNNaNNaNNaN
62021-01-26 03:10:505567592021-01-26 00:00:002021-01-26 03:11:182021-01-26 03:12:052021-01-26 03:19:312021-01-26 03:26:0488512.8091003.4045.045.000.01460.0NaNNaNNaNNaNNaN
72021-01-26 03:14:105567602021-01-26 00:00:002021-01-26 03:14:382021-01-26 03:14:442021-01-26 03:33:532021-01-26 03:42:3884930.0256810.1654.053.001.0270.0NaNNaNNaN44.166667NaN
82021-01-26 03:14:205567612021-01-26 00:00:002021-01-26 03:14:502021-01-26 03:15:142021-01-26 04:00:022021-01-26 04:13:31115432.4442002.8629.029.000.0955.0NaNNaNNaN2.500000NaN
92021-01-26 03:15:185567622021-01-26 00:00:002021-01-26 03:21:272021-01-26 03:22:042021-01-26 04:14:562021-01-26 04:38:39210372.8786002.61NaNNaN0NaN1.0autoReassignment Request from SE portal.1.0NaNNaN

Last rows

order_timeorder_idorder_dateallot_timeaccept_timepickup_timedelivered_timerider_idfirst_mile_distancelast_mile_distancealloted_ordersdelivered_orderscancelledundelivered_orderslifetime_order_countreassignment_methodreassignment_reasonreassigned_ordersession_timecancelled_time
4499902021-02-06 10:03:001302212021-02-06 00:00:002021-02-06 10:03:012021-02-06 10:04:032021-02-06 10:07:442021-02-06 10:15:0336172.19651.2422.022.000.034.0NaNNaNNaN376.416667NaN
4499912021-02-06 10:03:011302222021-02-06 00:00:002021-02-06 10:04:302021-02-06 10:06:042021-02-06 10:26:232021-02-06 10:36:097731.47071.12147.0146.001.0773.0NaNNaNNaN268.166667NaN
4499922021-02-06 10:03:061302232021-02-06 00:00:002021-02-06 10:03:472021-02-06 10:04:072021-02-06 10:15:012021-02-06 10:26:0614070.30071.26265.0264.001.0477.0NaNNaNNaN209.266667NaN
4499932021-02-06 10:03:111302242021-02-06 00:00:002021-02-06 10:03:122021-02-06 10:03:352021-02-06 10:15:432021-02-06 10:25:147452.70133.72200.0200.000.0290.0NaNNaNNaN229.250000NaN
4499942021-02-06 10:03:141302252021-02-06 00:00:002021-02-06 10:03:232021-02-06 10:03:432021-02-06 10:15:362021-02-06 10:34:1078231.79162.60202.0201.001.01426.0NaNNaNNaN273.233333NaN
4499952021-02-06 10:03:161302262021-02-06 00:00:002021-02-06 10:03:442021-02-06 10:04:142021-02-06 10:27:292021-02-06 10:44:0810060.57890.194.04.000.0127.0NaNNaNNaN369.516667NaN
4499962021-02-06 10:03:171302272021-02-06 00:00:002021-02-06 10:03:182021-02-06 10:04:342021-02-06 10:22:172021-02-06 10:31:432791.98631.1981.081.000.0105.0NaNNaNNaN239.133333NaN
4499972021-02-06 10:03:181302282021-02-06 00:00:002021-02-06 10:04:062021-02-06 10:04:392021-02-06 10:19:062021-02-06 10:26:5631611.59441.6128.028.000.01488.0NaNNaNNaN204.150000NaN
4499982021-02-06 10:03:191302292021-02-06 00:00:002021-02-06 10:03:192021-02-06 10:05:412021-02-06 10:20:392021-02-06 10:30:4193962.89394.6872.072.000.0105.0NaNNaNNaN65.583333NaN
4499992021-02-06 10:03:241302302021-02-06 00:00:002021-02-06 10:03:452021-02-06 10:05:142021-02-06 10:13:262021-02-06 10:19:4120781.89250.0930.030.000.0108.0NaNNaNNaN212.000000NaN